Publications by authors named "Paul A O'Gorman"

Article Synopsis
  • Climate change projections rely on physical models that struggle with small-scale processes, leading to uncertainties in predictions.
  • Recent machine learning algorithms show potential for improving these models but often fail when applied to new climate conditions they weren't originally trained on.
  • The proposed "climate-invariant" ML framework integrates physical knowledge into machine learning, enhancing accuracy and adaptability across various climate scenarios and improving Earth system modeling.
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A large number of recent studies have aimed at understanding short-duration rainfall extremes, due to their impacts on flash floods, landslides and debris flows and potential for these to worsen with global warming. This has been led in a concerted international effort by the INTENSE Crosscutting Project of the GEWEX (Global Energy and Water Exchanges) Hydroclimatology Panel. Here, we summarize the main findings so far and suggest future directions for research, including: the benefits of convection-permitting climate modelling; towards understanding mechanisms of change; the usefulness of temperature-scaling relations; towards detecting and attributing extreme rainfall change; and the need for international coordination and collaboration.

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Global climate models represent small-scale processes such as convection using subgrid models known as parameterizations, and these parameterizations contribute substantially to uncertainty in climate projections. Machine learning of new parameterizations from high-resolution model output is a promising approach, but such parameterizations have been prone to issues of instability and climate drift, and their performance for different grid spacings has not yet been investigated. Here we use a random forest to learn a parameterization from coarse-grained output of a three-dimensional high-resolution idealized atmospheric model.

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The circulation of the Northern Hemisphere extratropical troposphere has changed over recent decades, with marked decreases in extratropical cyclone activity and eddy kinetic energy (EKE) in summer and increases in the fraction of precipitation that is convective in all seasons. Decreasing EKE in summer is partly explained by a weakening meridional temperature gradient, but changes in vertical temperature gradients and increasing moisture also affect the mean available potential energy (MAPE), which is the energetic reservoir from which extratropical cyclones draw. Furthermore, the relation of changes in mean thermal structure and moisture to changes in convection associated with extratropical cyclones is poorly understood.

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In recent decades, the land surface has warmed substantially more than the ocean surface, and relative humidity has fallen over land. Amplified warming and declining relative humidity over land are also dominant features of future climate projections, with implications for climate-change impacts. An emerging body of research has shown how constraints from atmospheric dynamics and moisture budgets are important for projected future land-ocean contrasts, but these ideas have not been used to investigate temperature and humidity records over recent decades.

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Article Synopsis
  • Human actions are significantly impacting the Earth's climate.
  • Recognition of this issue is now accepted in international discussions.
  • Climate research must focus on new challenges and areas of study moving forward.
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The response of precipitation extremes to climate change is considered using results from theory, modeling, and observations, with a focus on the physical factors that control the response. Observations and simulations with climate models show that precipitation extremes intensify in response to a warming climate. However, the sensitivity of precipitation extremes to warming remains uncertain when convection is important, and it may be higher in the tropics than the extratropics.

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The coupling of proline- and azetidinone-substituted alkenes to 2-azidobenzoic and 2-azidobenzenesulfonic acid gives precursors that undergo intramolecular azide to alkene 1,3-dipolar cycloadditions to give imine-, triazoline- or aziridine-containing pyrrolo[1,4]benzodiazepines (PBDs), pyrrolo[1,2,5]benzothiadiazepines (PBTDs), and azetidino[1,4]benzodiazepines. The imines and aziridines are formed after loss of nitrogen from a triazoline cycloadduct. The PBDs are a potent class of antitumour antibiotics.

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Snowfall is an important element of the climate system, and one that is expected to change in a warming climate. Both mean snowfall and the intensity distribution of snowfall are important, with heavy snowfall events having particularly large economic and human impacts. Simulations with climate models indicate that annual mean snowfall declines with warming in most regions but increases in regions with very low surface temperatures.

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Transient eddies in the extratropical storm tracks are a primary mechanism for the transport of momentum, energy, and water in the atmosphere, and as such are a major component of the climate system. Changes in the extratropical storm tracks under global warming would impact these transports, the ocean circulation and carbon cycle, and society through changing weather patterns. I show that the southern storm track intensifies in the multimodel mean of simulations of 21st century climate change, and that the seasonal cycle of storm-track intensity increases in amplitude in both hemispheres.

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Global warming is expected to lead to a large increase in atmospheric water vapor content and to changes in the hydrological cycle, which include an intensification of precipitation extremes. The intensity of precipitation extremes is widely held to increase proportionately to the increase in atmospheric water vapor content. Here, we show that this is not the case in 21st-century climate change scenarios simulated with climate models.

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